Quantitative prediction of grain boundary thermal conductivities from local atomic environments

Abstract

Connecting grain boundary structures to macroscopic thermal behaviour is an important step in materials analysis and design. Here the authors develop a physical model combined with a machine-learning approach to accurately predict thermal conductivities of various types of MgO grain boundaries.